scholarly journals Comparative analysis of single cell lung atlas of bat, cat, tiger and pangolin

2021 ◽  
Author(s):  
Xiran Wang ◽  
Zhihua Ou ◽  
Peiwen Ding ◽  
Chengcheng Sun ◽  
Daxi Wang ◽  
...  
Keyword(s):  
Immune System ◽  
Comparative Analysis ◽  
Single Cell ◽  
Animal Species ◽  
Respiratory Viruses ◽  
Virus Infections ◽  
Species Comparison ◽  
Transcriptomic Data ◽  
Horseshoe Bat ◽  
Horseshoe Bats

Horseshoe bats (Rhinolophus sinicus) might help maintain coronaviruses severely affecting human health, such as SARS-CoV and SARS-CoV-2. It has long been suggested that bats may be more tolerant of viral infection than other mammals due to their unique immune system, but the exact mechanism remains to be fully explored. During the COVID-19 pandemic, multiple animal species were diseased by SARS-CoV-2 infection, especially in the respiratory system. Herein, single-cell transcriptomic data of the lungs of a horseshoe bat, a cat, a tiger, and a pangolin were generated. The receptor distribution of twenty-eight respiratory viruses belonging to fourteen viral families were characterized for the four species. Comparison on the immune-related transcripts further revealed limited cytokine activations in bats, which might explain the reason why bats experienced only mild diseases or even no symptoms upon virus infection. Our findings might increase our understanding of the immune background of horseshoe bats and their insensitivity to virus infections.

scholarly journals CytoTalk: De novo construction of signal transduction networks using single-cell transcriptomic data

2021 ◽  
Vol 7 (16) ◽  
pp. eabf1356
Author(s):  
Yuxuan Hu ◽  
Tao Peng ◽  
Lin Gao ◽  
Kai Tan
Keyword(s):  
Signal Transduction ◽  
Comparative Analysis ◽  
Single Cell ◽  
Developmental Stages ◽  
De Novo ◽  
Receptor Gene ◽  
Signaling Networks ◽  
Sequencing Data ◽  
Transcriptomic Data ◽  
Steiner Forest

Single-cell technology enables study of signal transduction in a complex tissue at unprecedented resolution. We describe CytoTalk for de novo construction of cell type–specific signaling networks using single-cell transcriptomic data. Using an integrated intracellular and intercellular gene network as the input, CytoTalk identifies candidate pathways using the prize-collecting Steiner forest algorithm. Using high-throughput spatial transcriptomic data and single-cell RNA sequencing data with receptor gene perturbation, we demonstrate that CytoTalk has substantial improvement over existing algorithms. To better understand plasticity of signaling networks across tissues and developmental stages, we perform a comparative analysis of signaling networks between macrophages and endothelial cells across human adult and fetal tissues. Our analysis reveals an overall increased plasticity of signaling networks across adult tissues and specific network nodes that contribute to increased plasticity. CytoTalk enables de novo construction of signal transduction pathways and facilitates comparative analysis of these pathways across tissues and conditions.

scholarly journals MBRS-46. CHARTING NEOPLASTIC AND IMMUNE CELL HETEROGENEITY IN HUMAN AND GEM MODELS OF MEDULLOBLASTOMA USING scRNAseq

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii406-iii406
Author(s):  
Andrew Donson ◽  
Kent Riemondy ◽  
Sujatha Venkataraman ◽  
Ahmed Gilani ◽  
Bridget Sanford ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Immune Cell ◽  
Genetically Engineered ◽  
Cellular Heterogeneity ◽  
Cell Heterogeneity ◽  
Transcriptomic Data ◽  
Single Cell Rna Sequencing ◽  
Transcript Profiles

Abstract We explored cellular heterogeneity in medulloblastoma using single-cell RNA sequencing (scRNAseq), immunohistochemistry and deconvolution of bulk transcriptomic data. Over 45,000 cells from 31 patients from all main subgroups of medulloblastoma (2 WNT, 10 SHH, 9 GP3, 11 GP4 and 1 GP3/4) were clustered using Harmony alignment to identify conserved subpopulations. Each subgroup contained subpopulations exhibiting mitotic, undifferentiated and neuronal differentiated transcript profiles, corroborating other recent medulloblastoma scRNAseq studies. The magnitude of our present study builds on the findings of existing studies, providing further characterization of conserved neoplastic subpopulations, including identification of a photoreceptor-differentiated subpopulation that was predominantly, but not exclusively, found in GP3 medulloblastoma. Deconvolution of MAGIC transcriptomic cohort data showed that neoplastic subpopulations are associated with major and minor subgroup subdivisions, for example, photoreceptor subpopulation cells are more abundant in GP3-alpha. In both GP3 and GP4, higher proportions of undifferentiated subpopulations is associated with shorter survival and conversely, differentiated subpopulation is associated with longer survival. This scRNAseq dataset also afforded unique insights into the immune landscape of medulloblastoma, and revealed an M2-polarized myeloid subpopulation that was restricted to SHH medulloblastoma. Additionally, we performed scRNAseq on 16,000 cells from genetically engineered mouse (GEM) models of GP3 and SHH medulloblastoma. These models showed a level of fidelity with corresponding human subgroup-specific neoplastic and immune subpopulations. Collectively, our findings advance our understanding of the neoplastic and immune landscape of the main medulloblastoma subgroups in both humans and GEM models.

scholarly journals Respiratory viral infections in the elderly

2021 ◽  
Vol 15 ◽  
pp. 175346662199505
Author(s):  
Alastair Watson ◽  
Tom M. A. Wilkinson
Keyword(s):  
Immune System ◽  
Viral Infections ◽  
Point Of Care ◽  
The Elderly ◽  
Respiratory Viruses ◽  
Morbidity And Mortality ◽  
Detection Methods ◽  
Respiratory Viral Infections ◽  
Tract Infections ◽  
Prolonged Hospital

With the global over 60-year-old population predicted to more than double over the next 35 years, caring for this aging population has become a major global healthcare challenge. In 2016 there were over 1 million deaths in >70 year olds due to lower respiratory tract infections; 13–31% of these have been reported to be caused by viruses. Since then, there has been a global COVID-19 pandemic, which has caused over 2.3 million deaths so far; increased age has been shown to be the biggest risk factor for morbidity and mortality. Thus, the burden of respiratory viral infections in the elderly is becoming an increasing unmet clinical need. Particular challenges are faced due to the interplay of a variety of factors including complex multimorbidities, decreased physiological reserve and an aging immune system. Moreover, their atypical presentation of symptoms may lead to delayed necessary care, prescription of additional drugs and prolonged hospital stay. This leads to morbidity and mortality and further nosocomial spread. Clinicians currently have limited access to sensitive detection methods. Furthermore, a lack of effective antiviral treatments means there is little incentive to diagnose and record specific non-COVID-19 viral infections. To meet this unmet clinical need, it is first essential to fully understand the burden of respiratory viruses in the elderly. Doing this through prospective screening research studies for all respiratory viruses will help guide preventative policies and clinical trials for emerging therapeutics. The implementation of multiplex point-of-care diagnostics as a mainstay in all healthcare settings will be essential to understand the burden of respiratory viruses, diagnose patients and monitor outbreaks. The further development of novel targeted vaccinations as well as anti-viral therapeutics and new ways to augment the aging immune system is now also essential. The reviews of this paper are available via the supplemental material section.

scholarly journals COVID-19 mitigation strategies were associated with decreases in other respiratory virus infections

2021 ◽  
Author(s):  
Sinha Pranay ◽  
Katherine Reifler ◽  
Michael Rossi ◽  
Manish Sagar
Keyword(s):  
Respiratory Virus ◽  
Respiratory Infections ◽  
Respiratory Viruses ◽  
Mitigation Strategies ◽  
Virus Infections ◽  
Time Period ◽  
Viral Respiratory Infections ◽  
Respiratory Virus Infections ◽  
Viral Respiratory

Abstract Detection of diverse respiratory viruses in Boston was around 80% lower after practices were instituted to limit COVID-19 spread compared to the same time period during the previous five years. Continuing the strategies that lower COVID-19 dissemination may be useful in decreasing the incidence of other viral respiratory infections.

scholarly journals The airborne contagiousness of respiratory viruses: A comparative analysis and implications for mitigation

2021 ◽  
pp. 101285
Author(s):  
Alex Mikszewski ◽  
Luca Stabile ◽  
Giorgio Buonanno ◽  
Lidia Morawska
Keyword(s):  
Comparative Analysis ◽  
Respiratory Viruses

scholarly journals A web server for comparative analysis of single-cell RNA-seq data

2018 ◽  
Vol 9 (1) ◽  
Author(s):  
Amir Alavi ◽  
Matthew Ruffalo ◽  
Aiyappa Parvangada ◽  
Zhilin Huang ◽  
Ziv Bar-Joseph
Keyword(s):  
Comparative Analysis ◽  
Single Cell ◽  
Web Server ◽  
Rna Seq

scholarly journals Comparative analysis of antibody- and lipid-based multiplexing methods for single-cell RNA-seq

2020 ◽  
Author(s):  
Viacheslav Mylka ◽  
Jeroen Aerts ◽  
Irina Matetovici ◽  
Suresh Poovathingal ◽  
Niels Vandamme ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Comparative Analysis ◽  
Single Cell ◽  
Cell Lines ◽  
Clinical Studies ◽  
Clinical Samples ◽  
Rna Seq ◽  
Batch Effects ◽  
Single Cell Sequencing ◽  
Single Nucleus

ABSTRACTMultiplexing of samples in single-cell RNA-seq studies allows significant reduction of experimental costs, straightforward identification of doublets, increased cell throughput, and reduction of sample-specific batch effects. Recently published multiplexing techniques using oligo-conjugated antibodies or - lipids allow barcoding sample-specific cells, a process called ‘hashing’. Here, we compare the hashing performance of TotalSeq-A and -C antibodies, custom synthesized lipids and MULTI-seq lipid hashes in four cell lines, both for single-cell RNA-seq and single-nucleus RNA-seq. Hashing efficiency was evaluated using the intrinsic genetic variation of the cell lines. Benchmarking of different hashing strategies and computational pipelines indicates that correct demultiplexing can be achieved with both lipid- and antibody-hashed human cells and nuclei, with MULTISeqDemux as the preferred demultiplexing function and antibody-based hashing as the most efficient protocol on cells. Antibody hashing was further evaluated on clinical samples using PBMCs from healthy and SARS-CoV-2 infected patients, where we demonstrate a more affordable approach for large single-cell sequencing clinical studies, while simultaneously reducing batch effects.

scholarly journals Integrating multimodal data sets into a mathematical framework to describe and predict therapeutic resistance in cancer

2020 ◽  
Author(s):  
Kaitlyn Johnson ◽  
Grant R. Howard ◽  
Daylin Morgan ◽  
Eric A. Brenner ◽  
Andrea L. Gardner ◽  
...  
Keyword(s):  
Mathematical Models ◽  
Single Cell ◽  
Treatment Response ◽  
Model Parameters ◽  
Data Sets ◽  
Response Dynamics ◽  
Multimodal Data ◽  
Transcriptomic Data ◽  
Treatment Regimens ◽  
New Treatment

SummaryA significant challenge in the field of biomedicine is the development of methods to integrate the multitude of dispersed data sets into comprehensive frameworks to be used to generate optimal clinical decisions. Recent technological advances in single cell analysis allow for high-dimensional molecular characterization of cells and populations, but to date, few mathematical models have attempted to integrate measurements from the single cell scale with other data types. Here, we present a framework that actionizes static outputs from a machine learning model and leverages these as measurements of state variables in a dynamic mechanistic model of treatment response. We apply this framework to breast cancer cells to integrate single cell transcriptomic data with longitudinal population-size data. We demonstrate that the explicit inclusion of the transcriptomic information in the parameter estimation is critical for identification of the model parameters and enables accurate prediction of new treatment regimens. Inclusion of the transcriptomic data improves predictive accuracy in new treatment response dynamics with a concordance correlation coefficient (CCC) of 0.89 compared to a prediction accuracy of CCC = 0.79 without integration of the single cell RNA sequencing (scRNA-seq) data directly into the model calibration. To the best our knowledge, this is the first work that explicitly integrates single cell clonally-resolved transcriptome datasets with longitudinal treatment response data into a mechanistic mathematical model of drug resistance dynamics. We anticipate this approach to be a first step that demonstrates the feasibility of incorporating multimodal data sets into identifiable mathematical models to develop optimized treatment regimens from data.

scholarly journals Aging and Options to Halt Declining Immunity to Virus Infections

2021 ◽  
Vol 12 ◽  
Author(s):  
Miguel Ángel Palacios-Pedrero ◽  
Albert D. M. E. Osterhaus ◽  
Tanja Becker ◽  
Husni Elbahesh ◽  
Guus F. Rimmelzwaan ◽  
...  
Keyword(s):  
Older Adults ◽  
Immune System ◽  
Immune Function ◽  
Adaptive Immunity ◽  
Bacterial Infections ◽  
Viral Infections ◽  
Age Group ◽  
Virus Infections ◽  
Innate And Adaptive Immunity ◽  
Chronic Viral Infections

Immunosenescence is a process associated with aging that leads to dysregulation of cells of innate and adaptive immunity, which may become dysfunctional. Consequently, older adults show increased severity of viral and bacterial infections and impaired responses to vaccinations. A better understanding of the process of immunosenescence will aid the development of novel strategies to boost the immune system in older adults. In this review, we focus on major alterations of the immune system triggered by aging, and address the effect of chronic viral infections, effectiveness of vaccination of older adults and strategies to improve immune function in this vulnerable age group.

scholarly journals Enabling out-of-clinic human immunity studies via single-cell profiling of capillary blood

2020 ◽  
Author(s):  
Tatyana Dobreva ◽  
David Brown ◽  
Jong Hwee Park ◽  
Matt Thomson
Keyword(s):  
Gene Expression ◽  
Immune System ◽  
Environmental Factors ◽  
Single Cell ◽  
Immune Cell ◽  
Cell Types ◽  
Capillary Blood ◽  
Specific Gene ◽  
Human Immune ◽  
Over Time

AbstractAn individual’s immune system is driven by both genetic and environmental factors that vary over time. To better understand the temporal and inter-individual variability of gene expression within distinct immune cell types, we developed a platform that leverages multiplexed single-cell sequencing and out-of-clinic capillary blood extraction to enable simplified, cost-effective profiling of the human immune system across people and time at single-cell resolution. Using the platform, we detect widespread differences in cell type-specific gene expression between subjects that are stable over multiple days.SummaryIncreasing evidence implicates the immune system in an overwhelming number of diseases, and distinct cell types play specific roles in their pathogenesis.1,2 Studies of peripheral blood have uncovered a wealth of associations between gene expression, environmental factors, disease risk, and therapeutic efficacy.4 For example, in rheumatoid arthritis, multiple mechanistic paths have been found that lead to disease, and gene expression of specific immune cell types can be used as a predictor of therapeutic non-response.12 Furthermore, vaccines, drugs, and chemotherapy have been shown to yield different efficacy based on time of administration, and such findings have been linked to the time-dependence of gene expression in downstream pathways.21,22,23 However, human immune studies of gene expression between individuals and across time remain limited to a few cell types or time points per subject, constraining our understanding of how networks of heterogeneous cells making up each individual’s immune system respond to adverse events and change over time.

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